Digital assistant

ABSTRACT

In one aspect, a server that receives, from a client terminal via a network, a request to initiate a verbal conversation using natural language that is in a spoken or textual format, extracts information during the verbal conversation, determines a context of the verbal conversation, receives an inquiry during the verbal conversation, processes the inquiry, acquires response information based on the determined appropriate response, and transmits to the client terminal the response information.

CROSS REFERENCE TO RELATED APPLICATION(S)

The present application is a Continuation application of U.S.application Ser. No. 16/122,875, filed Sep. 5, 2018, which is based onand claims the benefit from U.S. Provisional Application No. 62/554,236,filed Sep. 5, 2017, the entire contents of each of which areincorporated hereby by reference.

BACKGROUND

The “background” description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent it is described in thisbackground section, as well as aspects of the description which may nototherwise qualify as prior art at the time of filing, are neitherexpressly or impliedly admitted as prior art against the presentinvention.

The present specification relates generally to digital assistants, andmore specifically, to a server, method, and computer-readable storagemedium implementing a digital assistant.

Recently, voice- or text-based digital assistants have been introducedinto the marketplace to handle various tasks such as web searching andnavigation. However, often times, these digital assistants do notprovide accurate results in an efficient and robust manner.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the disclosure and many of the attendantadvantages thereof will be readily obtained as the same becomes betterunderstood by reference to the following detailed description whenconsidered in connection with the accompanying drawings, wherein:

FIG. 1 depicts an exemplary schematic of a server and a client terminal,according to one or more aspects of the disclosed subject matter;

FIG. 2 depicts an exemplary block diagram of a system for screeningservices according to one or more aspects of the disclosed subjectmatter;

FIG. 3 depicts an exemplary architecture for the screening servicessystem according to one or more aspects of the disclosed subject matter;

FIG. 4 illustrates a flow chart, according to an exemplary aspect of thedisclosed subject matter;

FIG. 5 illustrates a flow chart of the learning method, according to anexemplary aspect of the disclosed subject matter;

FIG. 6 illustrates a block diagram of an Emma server, according to anexemplary aspect of the disclosed subject matter; and

FIG. 7 is a hardware block diagram of a server according to one or moreexemplary aspects of the disclosed subject matter.

DETAILED DESCRIPTION

The description set forth below in connection with the appended drawingsis intended as a description of various embodiments of the disclosedsubject matter and is not necessarily intended to represent the onlyembodiment(s). In certain instances, the description includes specificdetails for the purpose of providing an understanding of the disclosedsubject matter. However, it will be apparent to those skilled in the artthat embodiments may be practiced without these specific details. Insome instances, well-known structures and components may be shown inblock diagram form in order to avoid obscuring the concepts of thedisclosed subject matter.

Reference throughout the specification to “one embodiment” or “anembodiment” means that a particular feature, structure, characteristic,operation, or function described in connection with an embodiment isincluded in at least one embodiment of the disclosed subject matter.Thus, any appearance of the phrases “in one embodiment” or “in anembodiment” in the specification is not necessarily referring to thesame embodiment. Further, the particular features, structures,characteristics, operations, or functions may be combined in anysuitable manner in one or more embodiments. Further, it is intended thatembodiments of the disclosed subject matter can and do covermodifications and variations of the described embodiments.

It must be noted that, as used in the specification and the appendedclaims, the singular forms “a,” “an,” and “the” include plural referentsunless the context clearly dictates otherwise. That is, unless clearlyspecified otherwise, as used herein the words “a” and “an” and the likecarry the meaning of “one or more.” Additionally, it is to be understoodthat terms such as “left,” “right,” “top,” “bottom,” “front,” “rear,”“side,” “height,” “length,” “width,” “upper,” “lower,” “interior,”“exterior,” “inner,” “outer,” and the like that may be used herein,merely describe points of reference and do not necessarily limitembodiments of the disclosed subject matter to any particularorientation or configuration. Furthermore, terms such as “first,”“second,” “third,” etc., merely identify one of a number of portions,components, points of reference, operations and/or functions asdescribed herein, and likewise do not necessarily limit embodiments ofthe disclosed subject matter to any particular configuration ororientation.

According to embodiments of the disclosed subject matter, an intelligentassistant for screening services can understand the screening domainvocabulary and idioms. As a result, the intelligent assistant can sensethe intent of an interacting user and perform, trigger, or enable thescreening services with a screening provider. Such assistant may beapplied in various fields such as employee screening. Employee screeningis an important part of Human Resources. For example, employee screeningcan include a criminal background check, a credit check, drug tests, andthe like. After the employee screening information is collected, theemployee screening information still requires verification, routinemonitoring, further analysis, and the like.

Referring now to the drawings, wherein like reference numerals designateidentical or corresponding parts throughout the several views.

FIG. 1 shows a server 100 and a client terminal (or client device) 110,according to an exemplary aspect of the disclosed invention. Note thatmore than one client device(s) 110 can be used to communicate with theserver 100. It is to be understood that there may be a plurality of eachof these aforementioned components. The client terminal 110 maycommunicate with apparatus 100 via one or more networks (for example, alocal area network (LAN) and/or another communications network, such asthe Internet).

Server 100 may be a server, computer (personal computer (PC), laptop,netbook, etc.), or the like which may operate with Apple's operatingsystem, Microsoft's Windows®, Google's Android®, Linux, or otheroperating system software. Server 100 may include the configurationshown in FIG. 2 . The server 100 can include processing circuitry.

FIG. 2 depicts an exemplary block diagram of a system 200 for screeningservices according to one or more aspects of the disclosed subjectmatter. The server 100 can be configured to host said screening serviceson said processing circuitry. Screening services can include backgroundscreening, as well as screenings for employment, residential,assessments and other information verification and due diligence needs,for example. Additionally, the screening services can include otherscreenings, information verification, and due diligence needs such asresidential, fleet management, and tax credits, for example. Thescreening services can also be compatible with vendors including courtrunners, 3^(rd) party verification providers, and collection sites. Thescreening services can also support multi-lingual capabilities and canbe applied globally as a result. The multi-lingual support can be basedon machine driven language translation services to support alanguage-of-choice for the users.

The screening services can be a speech, voice, and natural languageinterface, as well as providing seamless screening services and insightsto both customer and industry data.

In one aspect, the server 100 can provide intelligence services for thescreening services industry. The screening services industry is anindustry under the Human Resources vertical that deals with collecting,verifying, routine monitoring, assessments and analytics from sensitiveand personally identifiable information (PII) to publicly accessibleinformation in a secure manner which builds a workplace that'strustworthy, safe, and healthy. An intelligent assistant for screeningservices can understand the screening domain vocabulary and idioms, aswell as sense the intent of an interacting user and perform, trigger, orenable such services with a screening provider as a result.

Types of background services can include criminal background checks,credit checks, drug tests, and the like. Further, background servicescan include analytical and exploratory services include programmanagement and insights.

FIG. 3 depicts an exemplary architecture for the screening servicessystem according to one or more aspects of the disclosed subject matter.

The screening services architecture can include event processors, JIRAbased approval/assignment workflows, event relays/proxies, a botplatform, a conversation engine, and approved and authorized interactionchannels.

In one aspect, the server 100 can provide a screening to/for consumers,companies, job applicants, enablers and the like. The user terminal 110can include a speech interface/dialog to capture spoken voice or typednatural language text, wherein the spoken voice or natural language textcan outline the intent of the consumer, for example. The screeningservices can also collect additional information not available in thespoken intent as suitable to the consumer. The additional informationcan include required and/or missing additional information from thecandidates not already available with either the client or FirstAdvantage. Additionally, the additional information can includedocuments such as legal consents, authorizations, and other supportingdocuments, as well as additional information used to support follow-upprocesses such as consumer disputes on re-screen.

Additionally, the server 100 can derive an intent on what the service isand reach the right service provider such as First Advantage, forexample.

Further, the server 100 can provide intuitive and conversationalresponses to the consumer in voice, audio, graphics, text, hypermedia,and other supported response formats via the user terminal 110.

The server 100 can also build a screening vocabulary and idioms. Thescreening services could be developed in-house or integrated withexternal service providers that provide domain agnostic natural languageinterfaces with extension capabilities (e.g., Google API.AI platform).

The server 100 can be an out of the box system that can integrate withFirst Advantage Screening APIs, as well as other providers.

Additionally, the server 100 can provide an API for ordering actionslike creation of an order, cancellation, disputes, and the like.

Further, the server 100 can provide an API for providing data, bothpublic and PII, in a secure manner over secure communication channelsfrom recruiting companies, as well as a job applicants and employees.

In one aspect, the server 100 can host an automated conversation with auser, for example via the user terminal 110. For example, the server 100can receive user input from the user terminal 110, wherein the userterminal 110 can host the automated conversation via a messagingapplication, such as Skype, Facebook Messenger, Whatsapp, WeChat, Line,SMS, etc. In another non-limiting example, user input can be transmitteddirectly to the server 100. The server 100 can receive data from theuser terminal 110 in the form of myriad media, for example in the formof voice, text, tap, or any combination thereof. For example, thereceived data can include a request for user data. The server 100 cananalyze the data and transmit a predetermined response based on thereceived data, for example transmitting the requested user data.

For example, the server 100 can provide an API for determining candidatebackground screen status.

In one aspect, the server 100 receives a request from a user, forexample a job recruiter, for a predetermined set of data, for examplepotential job candidates. The server 100 can analyze a plurality ofparameters of the request and transmit the predetermined set of datasatisfying said plurality of parameters. For example, the server 100 canprovide an API for determining candidate qualifications.

In one aspect, the server 100 receives a request from a user, forexample a program manager reviewing the screening process, for apredetermined set of data, for example the status of the screeningprocess for a candidate. The server 100 can determine the status of thecandidate and transmit the predetermined set of data to the user. Forexample, the server 100 can transmit that the candidate background checkhas been completed. For example, the server 100 can transmit that thecandidate credit check has not been completed.

In one aspect, the server 100 can determine that a predetermined changeto a user profile is needed and transmits the recommendation to a user,for example the candidate, the recruiter, or the program manager. Thepredetermined change can be determined, for example, based on auser-initiated request, a scheduled audit of said user profile, or anycombination thereof.

FIG. 4 illustrates a flow chart, according to an exemplary aspect of thepresent disclosure. In step S500, the request from the user can beinitiated by the user via, for example, the user terminal 110. Theserver 100 can receive the request and initiate a conversation. Requestsreceived by the server 100 can be processed by the server 100 andresponses can be transmitted by the server 100 to the user. Based on theaccrued information in the conversation, or the amount of context, theaccuracy of the responses to the requests can increase.

The request from the user to initiate the conversation can be, forexample, a question, a greeting, or a statement requesting data from theserver 100. The server 100 can interface with the user through anintelligent assistant, for example named “Emma”, via the messagingapplication. For example, the user can initiate the conversation bytyping “Hi, Emma” into the messaging application. In step S505, theserver 100 can receive the user initiation and begin a conversation andcommunicating with the user through Emma.

In one aspect, the initiation of the conversation as received by theserver 100 can initialize a transcription or audio recording of theconversation to a memory, beginning with the first request. Subsequentcommunication, for example statements or questions from the user, canalso be transcribed or recorded by the server 100. Transcription orrecording can be terminated at the conclusion of the conversation whenthe server 100 receives an input from the user, for example a farewellphrase such as “Goodbye, Emma” or a phrase of the like.

After the server 100 receives the first request from the user andinitializes the conversation, the server 100 can transmit a firstresponse to the user to acknowledge the first request. For example, theserver 100 can reply with “Hello”. In one aspect, the server 100 canreceive predetermined data input from the user before receiving thefirst request. For example, the server 100 can request the user's name.The server 100 can respond with the user's name (e.g. “Beth”) in theserver's 100 first response to the first request, for example “Hello,Beth”.

The server 100 can transmit a second request for authenticating datafrom the user. For example, the second request can be “What is yoursocial security number?” The server 100 can receive a second responsefrom the user with the authenticating data. If the authenticating datais correct, the conversation can continue, and the server 100 canreceive a third request from the user. In one aspect, the server 100 maydetect that the user has already entered correct authentication datainto an existing application requiring authentication. This can allowthe server 100 to skip the transmission of the request forauthentication data from the user.

In one aspect, the server 100 can utilize the received authenticationdata to access a database storing user information. For example, thedatabase may be a private database including residence information,credit information, motor vehicle registration information, etc.

In one aspect, the server 100 can utilize the information in thedatabase to determine subsequent responses. The server 100 can check thedatabase information and perform an updated of a user profile based ondifferences between the user profile and the database information. Thedatabase information can be provided to the server 100 or accessedthrough some other entity or service. If the server 100 has control overthe service, the server can update the user profile itself or send arequest to a remote external device to perform the update. For example,the user profile may be available online via social media and a requestto update the user social media profile based on the databaseinformation can be transmitted by the server 100 to the social mediaservice. For example, the server 100 may ask the user “Are you stillliving at 123 Orchard Drive?” and based on a response from the user, theserver 100 may update the user's address.

The third request received from the user can be a question regarding theneed for the second request. For example, the user can ask “Why do youneed my social security number?”

In step S510, the server 100 can determine that the third request is anunknown query and attempt to infer a response based on a base set ofdefined phrases or utterances, known as an Emma Language System (ELS).If the answer can be inferred (S517), the server 100 can select ananswer to the third request and transmit a third response to the user instep S520. If the answer is unable to be inferred from the ELS (S517),the server 100 can initiate a learning process 600 (FIG. 5 ) in stepS525 to update the ELS.

The ELS can be a composite learning system that can self-learn, heal,accept supervised corrections from an external operator, and allowseeding of language elements in order to interface fluidly with externalusers. The ELS may define new screening entities, idioms, orassociations to existing entities. An entity can be a concept thatembodies a noun that represents a person, company, policy or other nounforms that has a role in background screening processes. Entities caninclude properties and behaviors that can be represented in naturallanguages and lead to a language system/workbench. Entities can besourced from Knowledge Sources like First Advantage Knowledgebase,Industry standards like HR XML/HR JSON, or really any qualified source,which can allow greater consistency in definition and improved clarityin representation.

Entities can be related to each other and form a relationship or graph.The server's 100 knowledge representation and graph system can form theunambiguous system of checks and relationships permissible. This canguide the server 100 in establishing the correctness or the goal of anutterance from the user.

If the server 100 can determine that the third request is a familiarquery (S510), the server can analyze the language of the request, thepreviously received data from the user, and transmitted data to the userfrom the server 100, i.e. the context of the conversation in step S515.For example, the context of the conversation can be a job candidatecommunicating with a potential employer. For example, the context of theconversation can be more specific within the field of jobcandidates/potential employer, such as a progress of the jobapplication, updating of a resume, submitting of academic transcripts,interview scheduling, position benefits, type of position, positiontitle, etc. For example, a user can submit a request to atelecommunications service provider, i.e. server 100, for an update to amobile device. For example, the user can desire a new mobile deviceitself or an update to the software on the mobile device. Without theparticular context within which the user is referring to, it isdifficult for the service provider to retrieve the most accurateresponse to the user when an upgrade is requested. Therefore, by takinginto account the context of the request during the conversation, theservice provider can provide a more accurate response to the user'sinquiry. For example, the user may state “I want a new phone”. As such,the service provider analyzes the language used throughout theconversation (or the particular environment of the conversation) inorder to determine the context i.e. is the user referring to updatingthe physical device to a new model or updating the operating software onthe mobile device.

In one embodiment, the context may not be the actual inquiry or questioni.e. update mobile device, but rather the conversation that precedes orfollows the inquiry which allows the server 100 to analyze theparticular environment that the question is posed in. Therefore theserver 100 is able to determine that the inquiry relates to updating ofthe device itself as opposed to updating something on the mobile devicee.g. operating software, applications, etc.

In step S530, if the context is similar to the language included in thethird request, the server 100 can continue with the previous contextualdata and preferences for determining a response in step S535. Forexample, the server 100 can transmit a response, such as “Your socialsecurity number helps verify your identity.” Otherwise, in step S540,the server 100 can determine that the context of the third request isunrelated to the previous conversational context (the previouslyreceived data from the user, and transmitted data to the user from theserver 100) and the server 100 can initiate new data and authorizationchecks. For example, the server 100 could receive a request for “What isthe weather like today?” and begin analyzing the new data received bythe server 100 as a new conversation context.

In one aspect, the server 100 determines that the new conversationcontext is sufficiently unrelated to the former conversation and deletesthe data received by the server 100, analyzed by the server 100, andtransmitted by the server 100 during the former conversation. The server100 can determine that the new conversation context is sufficientlyunrelated to the former conversation by applying a weighted value to theidioms and utterances of the former conversation and comparing it to aweighted value applied to the idioms and utterances of the newconversation and determining if a difference in the average weightedvalues between to the former conversation and new conversation isgreater than a predetermined threshold. The predetermined threshold canbe determined by a user based on the environment of the conversation ofthe screening services system 200.

In one aspect, a weighted value can be applied to a new inquiry, whereinthe server 100 can determine that the new inquiry is sufficientlyrelated to either the former conversation, the new conversation, or issufficiently unrelated to both and sets a new threshold for a second newconversation.

Note, inferring context continuations may be a complex process and mayrequire employing several techniques and algorithms, sometimes inparallel to ascertain a given statement is a continuation or related.Non-limiting examples may be:

a. Simple and Strong—Context Phrases/Idioms—Words that are usualcontinuations. For example, if the user says, “should I be carrying anumbrella?” right after asking “how's the weather?”—since umbrella is atrigger for rain relating to weather.

b. Broad but Weak—Possible responses by domain—Every starting statementeither leads to a possible context (or the server 100 may continuequerying to get to the context) and each such enrichment stage wouldhave possible responses, which would provide a sub-domain/sub-group ofstatements possible for a continuation. A search algorithm (heuristic orotherwise) that can probe continuity (like an autocomplete in a searchbox) would provide a maximum likelihood indicator for the continuity.

c. Weighted distance of deviation of context—If a weighted sum of theutterance deviations from a “current” context has higher distance, thenthe context is likely broken.

d. A combination of the above or other processes derived by machinelearning or Bayesian Inferences.

e. Any natural language technique that may be employed to improvise theaccuracy of context identification and switching.

It can be appreciated that other techniques may be used to determine thedifference in relevance between the idioms and the utterances of theformer conversation, new conversation, and inquiry context.

In step S545, the server 100 can determine that an appropriate responseis still unknown and initiate a procedure to interact with the user. Forexample, if the server receives a request for weather data, the server100 can transmit a fourth request for clarification of the third(weather) request. For example, the server 100 can transmit “Where isyour location?”

In one aspect, the server 100 can retain the former conversation dataand continually review the former conversation data when comparing thecontext of each user request. The server 100 can determine that thecontext of a user request does not have the same context as a recentconversation and instead has a context more similar to an earlierconversation and begin to analyze the request and determine and transmitan appropriate response based on the context of the earlierconversation. For example, the server 100 can receive a fifth requestfrom the user for further information related to the third request. Forexample, the fifth request can be “When was the social securityauthentication implemented?” and the server 100 can analyze the datafrom the first, second, and third requests (and responses) to determineand transmit an appropriate response to the fifth request. The server100 can determine that the fourth request is not in the same context andnot include the fourth request data in determining an appropriateresponse.

Advantageously, as the conversation lengthens, or as the amount of datareceived by the server 100, analyzed by the server 100, and transmittedby the server 100 increases, the increase in contextual informationregarding the user requests will increase the accuracy with which theserver 100 can determine and transmit an appropriate response.

In one aspect, the server 100 can utilize the information in thedatabase to facilitate task completion with the user. The server 100 canreceive an order request to perform a background check for a user. Basedon the database information, the server 100 can initiallycomplete/fill-in information necessary to perform a background check.For example, the server 100 can transmit a message to the userrequesting confirmation of personal information, such as “Is yourbirthday Jan. 1, 1980?” and based on a user reply, the server 100 canedit the preliminarily entered information accordingly or move onto thenext field of information required.

The server 100 can determine no further interactions are needed fordetermining an appropriate response and interact with a plurality ofdata sources to process an action in step S550. For example, the server100 can receive a request for a background check and the server 100 candetermine that all requirements are met by the user, such as sufficientpayment, required user identity information has been entered, etc. toprocess the request.

The server 100 can then infer a response and transmit the response tothe user (S520). In step S555, the server 100 can interact with the userto receive additional data or end the conversation.

The following is an exemplary conversation between a user (e.g., Scott)and the intelligent assistant “Emma”:

SCOTT: “Hello Emma”

EMMA: “Hey Scott ! Hello Collaborate . . . ! Nice to meet you all’

SCOTT: “Emma, tell the audience about yourself”

EMMA: “I am a natural language interface to FADV's products and willhelp you and your candidates through the Screening process . . . youjust need to ask me a question. Ask Emma will be part of future productreleases and will allow both Recruiters and Candidates to use voice inour products”

SCOTT: “That is so cool . . . . Emma, are you related to Siri or Alexa?

EMMA: “Yes, we are sisters . . . that was a joke”

SCOTT: “OK, we'll get into how you work in a minute . . . . Emma, what'sup at FADV?”

EMMA: “Well Scott! It was a great year for First Advantage . . . . Forstarters, we have a new Vision”

SCOTT: “What is the new vision?”

EMMA: “Deliver innovative solutions and insights that help our clientsmanage risk and hire the best talent”

SCOTT: “Wow, that is powerful. How will FADV make it happen?”

EMMA: “Good question, Scott. It is all part of the new FADV 3.0 strategyfocusing on Customer First, Globalization, and Innovation”

SCOTT: “Tell me about 2017 highlights”

EMMA: “Did you know FADV has already added over 150 new customers thisyear? Should I be pausing for cheers and claps . . . ?”

SCOTT: “That is amazing, what else is new?”

EMMA: “First Advantage is investing in new products and ProfileAdvantage and Insight Advantage are being launched”

SCOTT: “Tell me about Profile Advantage”

EMMA: “Profile Advantage provides a fantastic Candidate experience. Itis a mobile app for Candidates to enroll for Screening services. It is aone-of-a-kind app in the market and will reduce turn-around-times anddramatically improve the interaction and experience with Candidates. Youcan place an order, send an invite, get status, and so on”

SCOTT: “I love it. Tell me about Insight Advantage”

EMMA: “Insight Advantage provides our customers deeper insights intotheir programs worldwide all in one powerful dashboard. Let me show youan example”

SCOTT: “Emma, and you are part of these apps?”

EMMA: “Yes, for example, our customer can just speak into their phone toask the status of a Candidate and I can provide the answer”

SCOTT: “How do our customers learn more?”

EMMA: “Come see our tech team at the booth outside and they canexperience it first-hand”

SCOTT: “Thanks, Emma . . . you are awesome!”

EMMA: “Thanks, Scott . . . . I am blushing!”

SCOTT: “Goodbye Emma”

EMMA: “Talk to you later, Scott”

FIG. 5 illustrates a flow chart of the learning method 600, according toan exemplary aspect of the present disclosure. The learning process 600begins with a change event, wherein an unknown condition or event isdetermined and analysis of the event is desired in order to increase thedatabase of responses for the server 100 to transmit.

The unknown condition or event is analyzed in step S600. If a new entityis detected in step S605, the new entity is defined in step S610, thenthe associations and relationships related to the new entity are definedin step S615, and then the governing rules for the new entity aredefined in step S620 before the database of responses is updated in stepS699.

If a new entity is not detected (S625), a determination is made for ifan entity amendment exists in step S625. If an entity amendment exists,the entity definition is amended in step S630, then the associations andrelationships related to the new entity is updated in step S635, thenthe governing rules for the new entity are updated in step S640, andexisting learning is invalidated in step S645 before the database ofresponses is updated in step S699.

If a new entity amendment does not exist (S625), a determination is madefor if a relationship update related to the entity is needed in stepS650. If a relationship update is needed, a new entity is defined instep S655, then the associations and relationships related to the newentity are defined in step S660, and then the governing rules for thenew entity are defined in step S665 before the database of responses isupdated in step S699.

If a relationship update is not needed, an update can be applied via anexternal operator in step S670.

FIG. 6 illustrates a block diagram of an Emma server 700, according toan exemplary aspect of the present disclosure. A language source module705 includes a knowledge base 706, standards libraries 707, and otherlibrary sources 708. A knowledge graph module 710 includes an AI-poweredlearning module 711, a guided learning module 712, and other learningmodules 713. The language source module 705 can communicate with abackground screening domain language system 715. The knowledge graphmodule can also communicate with the background screening domainlanguage system 715. The background screening domain language system 715can communicate with a natural language interface 701. The naturallanguage interface 701 can communicate with at least one data sources720, for example an online database. The natural language interface 701can also communicate with voice input/output devices 725, otherintelligent assistants 730 (e.g. Alexa, SIRI, etc.), and other sources735.

Next, a hardware description of a computer/device (e.g., server 100)according to exemplary embodiments is described with reference to FIG. 7. The hardware description described herein can be a hardwaredescription of the server 100, client terminal 110, or any other devicesdiscussed herein. In FIG. 7 , the server 100 includes a CPU 300 whichperforms one or more of the processes described above/below. The processdata and instructions may be stored in memory 302. These processes andinstructions may also be stored on a storage medium disk 304 such as ahard drive (HDD) or portable storage medium or may be stored remotely.Further, the claimed advancements are not limited by the form of thecomputer-readable media on which the instructions of the inventiveprocess are stored. For example, the instructions may be stored on CDs,DVDs, in FLASH memory, RAM, ROM, PROM, EPROM, EEPROM, hard disk or anyother information processing device with which the server 100communicates, such as a server or computer.

Further, the claimed advancements may be provided as a utilityapplication, background daemon, or component of an operating system, orcombination thereof, executing in conjunction with CPU 300 and anoperating system such as Microsoft Windows, UNIX, Solaris, LINUX, AppleMAC-OS and other systems known to those skilled in the art.

The hardware elements in order to achieve the server 100 may be realizedby various circuitry elements. Further, each of the functions of theabove described embodiments may be implemented by circuitry, whichincludes one or more processing circuits. A processing circuit includesa particularly programmed processor, for example, processor (CPU) 300,as shown in FIG. 7 . A processing circuit also includes devices such asan application specific integrated circuit (ASIC) and conventionalcircuit components arranged to perform the recited functions.

In FIG. 7 , the server 100 includes a CPU 300 which performs theprocesses described above. The server 100 may be a general-purposecomputer or a particular, special-purpose machine. In one embodiment,the server 100 becomes a particular, special-purpose machine when theprocessor 300 is programmed to perform mobile keyboard content delivery.

Alternatively, or additionally, the CPU 300 may be implemented on anFPGA, ASIC, PLD or using discrete logic circuits, as one of ordinaryskill in the art would recognize. Further, CPU 300 may be implemented asmultiple processors cooperatively working in parallel to perform theinstructions of the inventive processes described above.

The server 100 in FIG. 7 also includes a network controller 306, such asan Intel Ethernet PRO network interface card from Intel Corporation ofAmerica, for interfacing with network 130. As can be appreciated, thenetwork 130 can be a public network, such as the Internet, or a privatenetwork such as an LAN or WAN network, or any combination thereof andcan also include PSTN or ISDN sub-networks. The network 130 can also bewired, such as an Ethernet network, or can be wireless such as acellular network including EDGE, 3G and 4G wireless cellular systems.The wireless network can also be WiFi, Bluetooth, or any other wirelessform of communication that is known.

The server 100 further includes a display controller 308, such as agraphics card or graphics adaptor for interfacing with display 310, suchas a monitor. A general purpose I/O interface 312 interfaces with akeyboard and/or mouse 314 as well as a touch screen panel 316 on orseparate from display 310. General purpose I/O interface also connectsto a variety of peripherals 318 including printers and scanners.

A sound controller 320 is also provided in the server 100 to interfacewith speakers/microphone 322 thereby providing sounds and/or music.

The general purpose storage controller 324 connects the storage mediumdisk 304 with communication bus 326, which may be an ISA, EISA, VESA,PCI, or similar, for interconnecting all of the components of the server100. A description of the general features and functionality of thedisplay 310, keyboard and/or mouse 314, as well as the displaycontroller 308, storage controller 324, network controller 306, soundcontroller 320, and general purpose I/O interface 312 is omitted hereinfor brevity as these features are known.

The exemplary circuit elements described in the context of the presentdisclosure may be replaced with other elements and structureddifferently than the examples provided herein. Moreover, circuitryconfigured to perform features described herein may be implemented inmultiple circuit units (e.g., chips), or the features may be combined incircuitry on a single chipset.

The functions and features described herein may also be executed byvarious distributed components of a system. For example, one or moreprocessors may execute these system functions, wherein the processorsare distributed across multiple components communicating in a network.The distributed components may include one or more client and servermachines, which may share processing, in addition to various humaninterface and communication devices (e.g., display monitors, smartphones, tablets, personal digital assistants (PDAs)). The network may bea private network, such as a LAN or WAN, or may be a public network,such as the Internet. Input to the system may be received via directuser input and received remotely either in real-time or as a batchprocess. Additionally, some implementations may be performed on modulesor hardware not identical to those described. Accordingly, otherimplementations are within the scope that may be claimed.

Having now described embodiments of the disclosed subject matter, itshould be apparent to those skilled in the art that the foregoing ismerely illustrative and not limiting, having been presented by way ofexample only. Thus, although particular configurations have beendiscussed herein, other configurations can also be employed. Numerousmodifications and other embodiments (e.g., combinations, rearrangements,etc.) are enabled by the present disclosure and are within the scope ofone of ordinary skill in the art and are contemplated as falling withinthe scope of the disclosed subject matter and any equivalents thereto.Features of the disclosed embodiments can be combined, rearranged,omitted, etc., within the scope of the invention to produce additionalembodiments. Furthermore, certain features may sometimes be used toadvantage without a corresponding use of other features. Accordingly,Applicant(s) intend(s) to embrace all such alternatives, modifications,equivalents, and variations that are within the spirit and scope of thedisclosed subject matter.

One embodiment is directed to a server that communicates with a clientterminal via a network, the server comprising: processing circuitryconfigured to receive, from the client terminal via the network, arequest to initiate a verbal conversation using natural language that isin a spoken or textual format, extract information during the verbalconversation, the extracted information being associated withcredentials of a user, an environment of the verbal conversation, and arequest to provide a response, determine a context of the verbalconversation based on the extracted information, receive an inquiryduring the verbal conversation, processing the inquiry by analyzing theinquiry in the determined context, and determining an appropriateresponse to the inquiry, acquire, locally or from an external device,response information based on the determined appropriate response,transmit to the client terminal, via the network, the responseinformation, receive, from the client terminal via the network, arequest to end the verbal conversation, transmit to the client terminal,via the network, an acknowledgment that the verbal conversation willend, and terminating the verbal conversation with the client terminal.

One embodiment is directed to a method performed by an apparatus thatcommunicates with a client terminal via a network, the methodcomprising: receiving, from the client terminal via the network, arequest to initiate a verbal conversation using natural language that isin a spoken or textual format; extracting information during the verbalconversation, the extracted information being associated withcredentials of a user, an environment of the verbal conversation, and arequest to provide a response; determining a context of the verbalconversation based on the extracted information; receiving an inquiryduring the verbal conversation; processing the inquiry by analyzing theinquiry in the determined context, and determining an appropriateresponse to the inquiry; acquiring, locally or from an external device,response information based on the determined appropriate response;transmitting to the client terminal, via the network, the responseinformation; receiving, from the client terminal via the network, arequest to end the verbal conversation; transmitting to the clientterminal, via the network, an acknowledgment that the verbalconversation will end; and terminating the verbal conversation with theclient terminal.

One embodiment is directed to a non-transitory computer-readable storagemedium including computer executable instructions, wherein theinstructions, when executed by a computer that communicates with aclient terminal via a network, causes the computer to perform a method,the method comprising: receiving, from the client terminal via thenetwork, a request to initiate a verbal conversation using naturallanguage that is in a spoken or textual format; extracting informationduring the verbal conversation, the extracted information beingassociated with credentials of a user, an environment of the verbalconversation, and a request to provide a response; determining a contextof the verbal conversation based on the extracted information; receivingan inquiry during the verbal conversation; processing the inquiry byanalyzing the inquiry in the determined context, and determining anappropriate response to the inquiry; acquiring, locally or from anexternal device, response information based on the determinedappropriate response; transmitting to the client terminal, via thenetwork, the response information; receiving, from the client terminalvia the network, a request to end the verbal conversation; transmittingto the client terminal, via the network, an acknowledgment that theverbal conversation will end; and terminating the verbal conversationwith the client terminal.

The invention claimed is:
 1. An apparatus that communicates with aclient terminal via a network, the apparatus comprising: processingcircuitry configured to receive, from the client terminal via thenetwork, a request to initiate a session that includes a verbalconversation using natural language that is in a spoken or textualformat, extract information during the verbal conversation, theextracted information being associated with credentials of a user, anenvironment of the verbal conversation, and a request to provide aresponse, determine a verbal conversation context of the verbalconversation based on the extracted information, assign a verbalconversation context value to the determined verbal conversationcontext, receive an inquiry during the verbal conversation, process theinquiry by determining an inquiry context of the inquiry, assigning aninquiry context value to the determined inquiry context, and in responseto a difference between the verbal conversation context value and theinquiry context value being less than or equal to a predeterminedthreshold, determining that the inquiry context of the inquiry isrelated to the verbal conversation context of the verbal conversationand generating an appropriate response related to the verbalconversation context of the verbal conversation, acquire, locally orfrom an external device, response information based on the generatedappropriate response related to the verbal conversation, and transmit tothe client terminal, via the network, the response information, whereinthe processing circuitry is further configured to generate theappropriate response related to the verbal conversation by determiningwhether a Language System (LS) includes a set of defined phrases orutterances related to the verbal conversation context of the verbalconversation, and upon determining the LS includes the set of definedphrases or utterances related to the verbal conversation context of theverbal conversation, the processing circuitry is further configured toamend an existing entity in the LS by updating at least one associationand at least one relationship related to the existing entity in the LS,update at least one governing rule for the existing entity in the LS,and terminate a learning process for the existing entity in the LS. 2.The apparatus according to claim 1, wherein the processing circuitry isfurther configured to transmit to the client terminal, via the network,an acknowledgment that the session will end.
 3. The apparatus accordingto claim 1, wherein the processing circuitry is further configured tostore a text or audio recording of the session to a memory.
 4. Theapparatus according to claim 3, wherein the recording comprises aplurality of portions, and the processing circuitry is furtherconfigured to separate each of the plurality of portions of therecording based on the verbal conversation context of the verbalconversation.
 5. The apparatus according to claim 1, wherein upondetermining the LS does not include the set of defined phrases orutterances related to the verbal conversation context of the verbalconversation, the processing circuitry is further configured to define anew entity in the LS, define at least one association and at least onerelationship related to the new entity in the LS, and define at leastone governing rule for the new entity in the LS.
 6. A method performedby an apparatus that communicates with a client terminal via a network,the method comprising: receiving, from the client terminal via thenetwork, a request to initiate a session that includes a verbalconversation using natural language that is in a spoken or textualformat; extracting information during the verbal conversation, theextracted information being associated with credentials of a user, anenvironment of the verbal conversation, and a request to provide aresponse; determining a verbal conversation context of the verbalconversation based on the extracted information; assigning a verbalconversation context value to the determined verbal conversationcontext; receiving an inquiry during the verbal conversation; processingthe inquiry by determining an inquiry context of the inquiry; assigningan inquiry context value to the determined inquiry context; and inresponse to a difference between the verbal conversation context valueand the inquiry context value being less than or equal to apredetermined threshold, determining that the inquiry context of theinquiry is related to the verbal conversation context of the verbalconversation and generating an appropriate response related to theverbal conversation context of the verbal conversation; acquiring,locally or from an external device, response information based on thegenerated appropriate response related to the verbal conversation; andtransmitting to the client terminal, via the network, the responseinformation, wherein the generating of the appropriate response relatedto the verbal conversation is performed by determining whether aLanguage System (LS) includes a set of defined phrases or utterancesrelated to the verbal conversation context of the verbal conversation,and upon determining the LS includes the set of defined phrases orutterances related to the verbal conversation context of the verbalconversation, amending an existing entity in the LS by updating at leastone association and at least one relationship related to the existingentity in the LS; updating at least one governing rule for the existingentity in the LS; and terminating a learning process for the existingentity in the LS.
 7. The method according to claim 6, further comprisingtransmitting to the client terminal, via the network, an acknowledgmentthat the session will end.
 8. The method according to claim 6, furthercomprising storing a text or audio recording of the session to a memory.9. The method according to claim 8, wherein the recording comprises aplurality of portions, and the method further comprises separating eachof the plurality of portions of the recording based on the verbalconversation context of the verbal conversation.
 10. The methodaccording to claim 6, further comprising upon determining the LS doesnot include the set of defined phrases or utterances related to theverbal conversation context of the verbal conversation, defining a newentity in the LS; defining at least one association and at least onerelationship related to the new entity in the LS; and defining at leastone governing rule for the new entity in the LS.
 11. A non-transitorycomputer-readable storage medium including computer executableinstructions, wherein the instructions, when executed by a computer thatcommunicates with a client terminal via a network, causes the computerto perform a method, the method comprising: receiving, from the clientterminal via the network, a request to initiate a session that includesa verbal conversation using natural language that is in a spoken ortextual format; extracting information during the verbal conversation,the extracted information being associated with credentials of a user,an environment of the verbal conversation, and a request to provide aresponse; determining a verbal conversation context of the verbalconversation based on the extracted information; assigning a verbalconversation context value to the determined verbal conversationcontext; receiving an inquiry during the verbal conversation; processingthe inquiry by determining an inquiry context of the inquiry; assigningan inquiry context value to the determined inquiry context; and inresponse to a difference between the verbal conversation context valueand the inquiry context value being less than or equal to apredetermined threshold, determining that the inquiry context of theinquiry is related to the verbal conversation context of the verbalconversation and generating an appropriate response related to theverbal conversation context of the verbal conversation; acquiring,locally or from an external device, response information based on thegenerated appropriate response related to the verbal conversation; andtransmitting to the client terminal, via the network, the responseinformation, wherein the generating of the appropriate response relatedto the verbal conversation is performed by determining whether aLanguage System (LS) includes a set of defined phrases or utterancesrelated to the verbal conversation context of the verbal conversation,and upon determining the LS includes the set of defined phrases orutterances related to the verbal conversation context of the verbalconversation, amending an existing entity in the LS by updating at leastone association and at least one relationship related to the existingentity in the LS; updating at least one governing rule for the existingentity in the LS; and terminating a learning process for the existingentity in the LS.
 12. The non-transitory computer-readable storagemedium according to claim 11, the method further comprising transmittingto the client terminal, via the network, an acknowledgment that thesession will end.
 13. The non-transitory computer-readable storagemedium according to claim 11, the method further comprising storing atext or audio recording of the session to a memory.
 14. Thenon-transitory computer-readable storage medium according to claim 13,wherein the recording comprises a plurality of portions, and the methodfurther comprises separating each of the plurality of portions of therecording based on the verbal conversation context of the verbalconversation.
 15. The non-transitory computer-readable storage mediumaccording to claim 11, the method further comprising upon determiningthe LS does not include the set of defined phrases or utterances relatedto the verbal conversation context of the verbal conversation, defininga new entity in the LS; defining at least one association and at leastone relationship related to the new entity in the LS; and defining atleast one governing rule for the new entity in the LS.